Bayesian Filtering Library 0.6.0 released

The Bayesian Filtering Library development team is pleased to announce the 0.6.0 release of BFL.
You can download this release from here and read the installation instructions online (also reachable through the orocos website).

This release includes support for lti, boost and newmat as matrix library and lti and boost as random number generator.
A new feature is the backward filter and smoother algorithm and the CPPUnit tests.
Furthermore for the first time, a step-by-step installation guide is available for Visual Studio on Windows.

The Bayesian Filtering Library (BFL) provides an application independent framework for inference in Dynamic Bayesian Networks, i.e., recursive information processing and estimation algorithms based on Bayes' rule, such as (Extended) Kalman Filters, Particle Filters (or Sequential Monte Carlo methods), etc. These algorithms can, for example, be run on top of the Realtime Services, or be used for estimation in Kinematics & Dynamics applications.

Since the links in the announcement of the BFL 0.6.0 release were not visible
in the previous email, I hereby send you the completed announcement.

Kind regards,

Tinne

The Bayesian Filtering Library development team is pleased to announce the
0.6.0 release of BFL.
You can download this release from and read
the installation instructions online at (also
reachable through the orocos website ).

This release includes support for lti, boost and newmat as matrix library and
lti and boost as random number generator.
A new feature is the backward filter and smoother algorithm and the CPPUnit
tests.
Furthermore for the first time, a step-by-step installation guide is available
for Visual Studio on Windows.

The Bayesian Filtering Library (BFL) provides an application independent
framework for inference in Dynamic Bayesian Networks, i.e., recursive
information processing and estimation algorithms based on Bayes' rule, such
as (Extended) Kalman Filters, Particle Filters (or Sequential Monte Carlo
methods), etc. These algorithms can, for example, be run on top of the
Realtime Services, or be used for estimation in Kinematics & Dynamics
applications.
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